In safety systems for motor vehicles, means for detecting subjects in front of the vehicle, for example pedestrians, bicyclists and/or animals, are known. However, such detection means are still susceptible to false detections which cause irritations of the driver and, more important, a user disbelief in the safety system, and, even more seriously, missed detections causing the system to fail to warn the driver. Furthermore, such detection means generally consume large processing capacities which is one reason for limited detection reliability.
The object of the invention is to provide a safety system for a motor vehicle with a high detection reliability and reduced probability of false detections.
In accordance with this invention, the classification of the surrounding vehicle environment into different categories allows adjusting the safety system depending on the determined vehicle environment. In particular, an algorithm for detecting a subject in front of the car may be optimally chosen or adjusted depending on the detected vehicle environment which leads to an increased detection reliability and less false detections. Furthermore, the invention allows the deactivation of algorithms in the control means which are considered irrelevant for the determined vehicle environment, which contributes to an effective use of processing resources and thereby to increased detection reliability.
The different predetermined categories of vehicle environment may comprise city environment, non-city environment, rural environment, highway environment, etc. Also sub-categories of these categories are possible. Preferably the environment classifying means is adapted to classify the vehicle environment at least into a city environment and a non-city environment.
Preferably the safety means is adapted to provide safety-related information to a vehicle occupant. A particular advantageous application of the invention relates to providing an animal warning to a vehicle occupant if an animal is detected in front of the vehicle. In such application, the environment classifying means is preferably adapted to determine a city environment of the vehicle. The animal detection means of said safety system is then preferably deactivated if a city environment is determined by the environment classifying means, because the probability of vehicle-animal collisions is assumed to be zero in a city environment. As a result, any false detection of a wild animal in a city environment is prevented. Furthermore, the processing resources saved due to deactivation of animal detection can be assigned to other processes, for example pedestrian detection, the reliability of which can thereby be further enhanced.
Furthermore, the environment classifying means is preferably adapted to determine a rural or non-city environment of the vehicle. The animal detection means of said safety system is then preferably activated if a rural or non-city environment is determined by said environment classifying means.
Other predetermined states than complete animal warning deactivation and activation may be selectable in the control means. In general, the control means is adjustable to one of a plurality of predetermined states depending on the determined vehicle environment.
In a preferred embodiment the environment category is determined on the basis of image data provided by an imaging means of said sensing arrangement. The imaging means may comprise one or a plurality of cameras directed to the area in front of the vehicle, in particular optical or infrared cameras. The vehicle environment category may then be determined from the detected image by using image analysis techniques, for example pattern recognition techniques which are known in principle in the art. Other methods for distinguishing between the predetermined vehicle environment categories may be employed.
The environment category may also be determined on the basis of vehicle data provided by one or more vehicle mounted sensors, for example the vehicle speed and/or yaw rate. For determining a city environment, for example, the fact may be used that the vehicle in the average travels slower and/or with a higher probability of turning in a city environment. Therefore, using the known velocity and/or yaw rate from a past time window classification of the vehicle environment into a city or non-city environment is possible.
In a particularly preferred embodiment of this invention, a combination of image data provided by an imaging means and vehicle data provided by at least one of vehicle sensor is used to determine the environment category. For example the image data may be used to determine the environment category and the vehicle data may then be used to confirm the detected environment category, which enhances the reliability of the detected vehicle environment category.